AI Implementation for Teams | Gabriel Omat
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Team implementation

Your team is already using AI. The question is how.

For companies of 10 to 50 people. I help leadership turn scattered, one-person-at-a-time AI use into a shared, responsible way of working that actually saves time.

Tell me about your team

The disconnect

It's already happening. Just not together.

Right now, someone in your company is asking AI for help with a sensitive email. Someone else is running an analysis they couldn't have done alone a year ago, or working through a problem they haven't quite figured out how to solve. Another person has quietly found a whole new way of working. And someone down the hall is still wondering whether AI is safe, useful, or even allowed in their job.

None of them are comparing notes. That's the disconnect most businesses are living with: AI is already in the building, but there's no shared direction behind it.

Responsible AI adoption starts with leadership.

If leadership isn't bought in, it won't stick. That's not because people don't care. It's because they take their cues from the top. When leaders use it, set clear expectations, and make time for it, the team follows.

So that's where we start: with you and your leadership team, agreeing on what AI is for in your business, what's off-limits, and what good looks like.

Shadow AI

The AI you can't see is the real risk.

When the approved tools don't fit the job, or nobody has explained what's allowed, people use whatever is in reach: personal accounts, free apps, client details pasted into a chat window. Most of the time it isn't rule-breaking. It's someone trying to get their work done.

Banning it doesn't work. What works is making the right way the easy way: a company workspace set up for the work people actually do, and plain guidance on what belongs in it and what doesn't.

Tools aren't enough

A login isn't a rollout.

Buying the seats is the easy part. A login doesn't teach anyone what to use AI for, how to use it well, or how their work connects to everyone else's. People need to be shown, on their own work, until it becomes how they do the job.

That's the difference between having AI and actually using it.

Pilots, not hype

If it doesn't save time, we don't use it.

We don't roll AI out everywhere at once. We pick a few real use cases, test them with the people who do that work, and look honestly at the results.

If it saves time or makes someone better at their job, it stays and we build on it. If it doesn't, we drop it. No AI for the sake of AI.

How it works

From scattered seats to a shared system.

Every engagement is scoped to your team, but the shape is the same.

  1. 01

    Start with leadership

    Agree on goals, guardrails, and what AI is (and isn't) for in your business, before anyone gets trained on anything.

  2. 02

    Find the work worth changing

    Talk with the people doing the work, see where the hours actually go, and choose a handful of pilot use cases.

  3. 03

    Pilot it on real work

    Build and test those use cases in the tools you already pay for. Keep what works, drop what doesn't.

  4. 04

    Build the shared foundation

    Turn what worked into one shared workspace: projects, company context, and clear guidance on what goes where.

  5. 05

    Train people on their own jobs

    Role-specific training, so everyone can use it, not just the two or three early adopters.

  6. 06

    Hand it over

    Your team leaves with a clear owner, a written plan, and the confidence to keep building.

What your team walks away with

Less friction. More forward.

  • Leadership aligned on what AI is for, and what it isn't
  • Clear guidance on what's safe and allowed
  • Pilots that proved their value on real work
  • A team that knows how to use AI in their own jobs
Portrait of Gabriel Omat

Who you'd be working with

I run my own business this way first.

I rebuilt my business around one AI workspace that knows my offers, my clients, and my week, and I've been helping other business owners build the same. Team implementation takes that approach to the whole company: shared context, shared workflows, and people who know how to use them.

Claude Certified Associate · Verified by Anthropic

More about me

Questions

Before we talk.

Where do we start?

With leadership. Before anyone gets trained on anything, we agree on what AI is for in your business and what the guardrails are. Everything else builds on that.

Do you work with ChatGPT or only Claude?

Both. Most of my own work runs in Claude, but the approach is the same in ChatGPT, and we build in whichever one your team already pays for.

Our team isn't technical. Is that a problem?

No. That's who this is for. Nothing here requires code. Your people learn to use AI on the work they already know how to do.

What does it cost?

Every engagement is scoped to the team: how many people, which departments, and how much we build. Send the form below and we'll work out what fits before any numbers come up.

How long does it take?

It depends on team size and scope. We'll map out a realistic timeline together on our first conversation.

We're smaller than 10 people, or bigger than 50. Can you help?

Maybe. Send a note anyway and I'll tell you honestly whether this is the right fit, or point you to something that is.

Get in touch

Tell me about your team.

Share anything you think would be helpful: how many people you have, which AI tools you're using, and where it is or isn't working yet. I'll get back to you within 24 hours.

Prefer email? hello@gabrielomat.com